Classification and Recognition of Ancient Glass Cultural Relics Based on K-Means Clustering
DOI:
https://doi.org/10.53469/jtpes.2024.04(01).05Keywords:
Classification of Ancient Glass, Principal Component Analysis, K-Means, Fuzzy Recognition, Chi-square TestAbstract
The qualitative analysis based on mathematical statistics and the quantitative analysis based on chi square test explored the relationship between the weathering of ancient glass artifacts and their colors, decorations, and types, respectively. It incorporates Principal Component Analysis (PCA), K-means clustering, and fuzzy analysis methods into the research on composition analysis and categorization of ancient glass products. With the aid of analysis of variance and mathematical statistical theory, a quantification table for sub-category divisions is established. Fuzzy mathematical methods, including fuzzy recognition, are applied to develop a sub-category identification model for ancient glass based on the quantification table. Finally, the model’s rationality and sensitivity are validated through the classification and identification of given sample data.
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Copyright (c) 2024 Jie Wang, Ting Yu, Qin Wang, Angxuan Gu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
